Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. And FlinkCEP is the Complex Event Processing (CEP) library implemented on top of Flink. Users can detect event patterns in streams of events.
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IBM Streams
Score 9.0 out of 10
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A real-time analytics solution that turns fast-moving volumes and varieties into insights. Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen. Its Eclipse-based, visual IDE lets solution architects visually build applications or use familiar programming languages like Java™, Scala or Python. Data engineers can connect with virtually any data source — whether…
In well-suited scenarios, I would recommend using Apache Flink when you need to perform real-time analytics on streaming data, such as monitoring user activities, analyzing IoT device data, or processing financial transactions in real-time. It is also a good choice in scenarios where fault tolerance and consistency are crucial. I would not recommend it for simple batch processing pipelines or for teams that aren't experienced, as it might be overkill, and the steep learning curve may not justify the investment.
Like the name says, it is good for streaming data and analyzing. It is great to look at tuples at a fast rate, filtering, calling other sources to enrich data, can call APIs, etc. Could do better for ingest use cases, can do better with guaranteed delivery, etc.
IBM Streams is well suited for providing wire-speed real-time end-to-end processing with sub-millisecond latency.
Streams is amazingly computationally efficient. In other words, you can typically do much more processing with a given amount of hardware than other technologies. In a recent linear-road benchmark Streams based application was able to provide greater capability than the Hadoop-based implementation using 10x less hardware. So even when latency isn't critical, using Streams might still make sense for reducing operational cost.
Streams comes out of the box with a large and comprehensive set of tested and optimized toolkits. Leveraging these toolkits not only reduces the development time and cost but also helps reduce project risk by eliminating the need for custom code which likely has not seen as much time in test or production.
In addition to the out of the box toolkits, there is an active developer community contributing additional specialized packages.
Python/SQL API, since both are relatively new, still misses a few features in comparison with the Java/Scala option
Steep Learning Curve, it's documentation could be improved to something more user-friendly, and it could also discuss more theoretical concepts than just coding
Apache Spark is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and fault tolerance, Apache Flink takes the lead. However, Spark has a larger community and a decidedly lower learning curve.
There are well explained tutorials to get the user started. If you are looking for business application ideas, the user community offers a diversity of applications. It is very easy to launch applications on the cloud and can integrate with other analytic tools available on Watson Studio. It takes away the burden of the technology so that users can focus on business innovations.
